Data on Crime Background

Unsurprisingly, they are highly correlated. Excluding the summary measure that is correlated with the four measures mechanically, the correlations among the four mea- sures range from 0.47 between Role Emotional and Vitality to 0.68 between Mental Health and Vitality. The top panel of Table 1 provides defi nitions of the lowest and highest possible scores of the four SF- 36’s mental health scales and reports the means and standard deviations of each of the measures. Most of the variation in the data is cross- sectional, though roughly a quarter of the variation is within an individual over time.

B. Data on Crime

Local area crime statistics are tabulated at the Local Government Area LGA level. LGAs in Australia are the third and lowest tier of government, administered by the states and territories, which, in turn, are beneath the Commonwealth or federal tier. Unlike the United States or the United Kingdom, there is only one level of local gov- ernment in all states, with no distinction such as counties and cities. We separately approached each state and territory government to request crime data. In some cases, this involved fi ling requests under the relevant Freedom of Information Acts, although these really served only to prompt the relevant data- holders, and ultimately none of the data were obtained in this manner. Eventually, we were able to obtain data for seven of the eight states and territories, covering 99 percent of the Australian popula- tion. Because the states do not apply a uniform crime classifi cation system, we re- coded crimes into 16 categories using the Australian Standard Offence Classifi cation ASOC, though, throughout the paper, our results are based on further aggregating these categories into violent and property crime. 4 With the restricted use version of the HILDA data set which contains information on the respondent’s postcode and the date of interview, we are able to match each individual to the crime rate in their local government area during the 12- month period before answering the questionnaire. In addition, the survey interviews individuals in each wave about whether they have been victims of crime, which allows us to distin- guish the responses of victims and nonvictims. In the bottom panel of Table 1, we present summary statistics on crime rates for the years 2002–2006. We distinguish between property crimes and violent crimes, a distinction that we will follow in our empirical specifi cations. Violent crimes include homicide, assault, sexual assault, abduction, and robbery. Property crimes include burglary and theft. Crime rates represent the crime incidents per 100,000 individuals in Australian metropolitan areas in the 12 months prior to the interview date. 5 As the fi rst row shows, the average violent crime rate in our data is 921 incidents per 100,000 individuals and 90 percent of the variation in crimes rates is across individuals. The remaining 10 percent is within individual over time. Property crime shows more varia- tion at the individual level where 17 percent derives from changes within individual over time and the remaining 83 percent refl ects cross- sectional variation. While not 4. See Data Appendix. 5. Note that this is not necessarily the same thing as LGA- year as individuals were interviewed at different points during the year. T he J ourna l of H um an Re sourc es 11 Table 1 Crime and Mental Well- being Variables Variable Mean Standard Deviation Overall Standard Deviation Between Standard Deviation Within V B V W Description SF 82.95 22.85 20.27 13.1 0.71 Social functioning: 0 = extreme and frequent interference with normal social activities due to physical or emotional problems; 100 = Performs normal social activities without interference due to physical or emotional problems. VT 60.62 19.46 17.52 9.69 0.77 Vitality: 0 = Feels tired and worn out all of the time; 100 = Feels full of pep and energy all of the time. RE 83.42 32.21 27.72 20.09 0.66 Role emotional: 0 = Problems with work or other daily activities as a result of emotional problems, 100 = No problems with work or other daily activities as a result of emotional problems. MH 74 16.98 15.44 8.84 0.75 Mental health: 0 = Feelings of nervousness and depression all of the time. 100 = Feels peaceful, happy, and calm all of the time. MCS 48.57 10.29 9.27 5.51 0.74 Mental component summary: Summary measure of mental well- being composed of a weighted average of the four measures defi ned above and then normalized to be between 1 – 100. PF 84.58 22.16 21.08 10.52 0.80 Physical function: 0 = Extremely limited in performing all physical function activities because of physical health; 100 = Performs physical function activities with ease Corna gl ia , F el dm an, a nd L ei gh 117 RP 80.92 34.42 29.39 20.29 0.68 Role physical: 0 = no problem with work or regular daily activities due to physical problems; 100 = extreme diffi - culty in performing work or regular daily activities due to physical problems. VCR 921.03 588.41 574.69 187.89 0.90 Violent crime rate: Violent crime incidents per 100,000 individuals in the 12 months prior to interview. PCR 5811.52 3067.07 2904.8 1299.87 0.83 Property crime rate: Property crime incidents per 100,000 individuals in the 12 months prior to interview. VC q1 0.0063 0.08 0.062 0.063 0.50 = 1 if victim of violent crime during previous 3 months, zero otherwise. VC q2–4 0.011 0.104 0.088 0.078 0.56 = 1 if victim of violent crime during previous 4 to 12 months, zero otherwise. PC q1 0.021 0.145 0.104 0.117 0.44 = 1 if victim of property crime during previous 3 months, zero otherwise. PC q2–4 0.039 0.194 0.147 0.152 0.48 = 1 if victim of property crime during previous 4 to 12 months, zero otherwise. Notes: V B V W represents the fraction of the variance that is due to between or cross- sectional variation compared to within over time variation. Sources: See Data Ap- pendix for crime rate data. Mental and physical well- being data and victimization obtained from HILDA 2001–2006. shown in this Table, property crime fell quite considerably over 2001–2006. 6 The criminology literature has not reached a consensus on the factors that explain this drop, though possible explanations include changes in the age structure, shifts in heroin supply, reduced availability of fi rearms, and improved antitheft devices in new motor vehicles. See, for example, Moffatt and Poynton 2006; Brickell 2008. Violent crime shows no such pattern. The next four rows of Table 1 present the fraction of respondents that were victim- ized during the quarter before the interview and or during the two to four quarters prior to the interview. Roughly 0.6 percent of our observations are violent crime vic- timization incidents within the previous quarter. 1.1 percent are victims two to four quarters before the interview. Property crime is more prevalent with 2.1 percent of individuals having suffered a property crime in the previous quarter and 3.9 percent in the two to four quarters before the interview. The identifying variation for these variables is roughly equally divided between cross- section and time, and the crime rate from these self- reported surveys is of a similar magnitude to police- reported crime rates.

C. Data on Individual Characteristics